Ritesh Shah Chief Architect – AI/ML/GenAI, Vanguard

Ritesh Shah is the Chief Architect for AI, Machine Learning, and Generative AI at Vanguard, bringing over 27 years of technology and architecture leadership experience spanning enterprise data, analytics, cloud platforms, AI, and machine learning. Throughout his career, he has led the design and transformation of large scale technology ecosystems that enable data-driven decision-making and innovation across the enterprise. Over the last decade, Ritesh has been at the forefront of architecting and scaling modern data and AI capabilities, including enterprise data platforms, data lakes, business intelligence and visualization solutions, cloud-based data and analytics platforms, and advanced AI/ML ecosystems. His expertise spans the end-to-end AI lifecycle - from data foundation and model development to production deployment, governance, observability, and operationalization at enterprise scale. Ritesh currently leads architecture strategy for Vanguard's AI and GenAI initiatives, with a focus on cloud-native AI/ML platforms, Generative AI, Agentic AI systems, and custom machine learning platforms. He has played a key role in enabling enterprise adoption of AI by designing scalable, secure, and governed platforms that accelerate experimentation while meeting the demands of reliability, compliance, and operational excellence. His work includes the development of enterprise AI solutions leveraging modern cloud AI services, foundation models, agent frameworks, retrieval-augmented generation (RAG), model evaluation platforms, and AI governance architectures. Passionate about translating emerging technologies into business value, Ritesh helps organizations navigate the rapidly evolving AI landscape while building trusted, production-ready AI solutions that deliver measurable outcomes. At AI4, Ritesh will share insights on how organizations can move beyond AI experimentation and establish a disciplined, scalable approach to evaluating and governing AI systems in production. Drawing from real-world enterprise experience, he will discuss how capabilities such as persona-based testing, Bring Your Own Judges (BYOJ), jury-based evaluations, human-in-the-loop review processes, adversarial testing, and continuous production monitoring can help organizations build trustworthy AI solutions. He will also showcase an enablement architecture that unifies evaluation, governance, observability, and operational workflows across the AI lifecycle, enabling teams to confidently measure, monitor, and improve AI systems from development through production.